AI Embeddings
Vector representations that capture the semantic meaning of text, images, or documents.
Definition
AI Embeddings are numerical vector representations that capture the semantic meaning of content—whether text, images, or documents—in a form that AI systems can process. In the AEC context, embeddings enable semantic search, similar document retrieval, and content clustering. Domain-specific embeddings trained on AEC data understand industry terminology and concepts, providing more relevant results than general-purpose embedding models.
In Depth
Embeddings are the mathematical foundation that makes semantic search and document AI possible. An embedding converts a piece of text (or an image, or a drawing) into a list of numbers — a vector — that captures its meaning. Texts with similar meanings get similar vectors, which is how the AI knows that "CMU" and "concrete masonry unit" are the same thing.
For AEC applications, the quality of embeddings depends heavily on training data. General-purpose embeddings trained on internet text understand that "foundation" can mean a building's structural base or a charitable organization. AEC-specific embeddings have been fine-tuned on construction documents, so they understand that in the context of a project specification, "foundation" always means the structural element — and they understand its relationships to related concepts like footings, grade beams, pile caps, and soil bearing capacity.
Embeddings also enable cross-format search. A drawing keynote that says "Type X GWB, 5/8 inch, see spec 09 29 00" can be matched against a spec section titled "Gypsum Board" even though the words are completely different. The embeddings capture the semantic relationship between the keynote reference and the specification content, making it possible to trace information across drawings, specs, and submittals automatically.
Examples
Creating embeddings of all project specifications for semantic search
Finding similar drawings by comparing their embedding vectors
Clustering project documents by topic using embedding similarity
Nomic Use Cases
See how Nomic applies this in production AEC workflows:
Frequently Asked Questions
AI Embeddings are numerical vector representations that capture the semantic meaning of content—whether text, images, or documents—in a form that AI systems can process. In the AEC context, embeddings enable semantic search, similar document retrieval, and content clustering. Domain-specific embeddings trained on AEC data understand industry terminology and concepts, providing more relevant results than general-purpose embedding models.
Creating embeddings of all project specifications for semantic search. Finding similar drawings by comparing their embedding vectors. Clustering project documents by topic using embedding similarity.
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